36
cluster, pixel data of each cluster were plotted in
the three dimensional principal component(PC) space
of the 1st to the 3rd PC. Figure 8(1) shows the
projected distribution of each cluster on to the
lst-2nd PC plane by looking from the positive side
of the 3rd PC. Figure 8(2) is the same image
except on to the 2nd-3rd PC from the negative side
of the 1st PC. As shown in Figure 8, each cluster
distributes continuously and this means that there
may be another possibility to get different
clusters by using different algorithm or parameters
of cluster analysis. To cope with this uncertainty
of clusters, 16 clusters were merged into 7 groups
by comparing seasonal vegetation dynamics of each
cluster. Figure 9 shows mean value curves of
monthly GVI data for each cluster in which similar
curves are drew in the same graph. Vertical scale
is not SNVI and is changed to NVI for better
understanding of vegetation dynamics. Figure 10
shows 7 groups of land cover in Asia.
Classified results (Figures 7 and 10) were
compared with DEM image (Figure 11), snow cover
data (for example Figure 12) and Wilson's land
cover data (for example Figure 13). Table 3 shows
land cover types and its distribution of each
cluster which were put in order with reference to
the above images and some documents (Justice 1985;
Willett 1987).
Classified results have the following
characteristics besides the ones described in Table
3.
Some part of boundaries of different land
cover clusters quite coincide with the elevation
pattern in Figure 11, for example Himalaya Range,
Plateau of Tibet, the Irrawaddy valley in Myanma,
plain in Indo-China Peninsula, Sulaiman Range in
Pakistan and Tien Shan Range.
Figure 12 is monthly snow cover images in
February 1983 which is one of the 12 monthly
images. It explains number of weeks with snow
cover in a specific month. By snow cover
data(Figure 12), it was found out that the low NVI
values from October to next April of land cover
group 3 in northern Eurasia are due to snow covers.
7. CHANGE DETECTION
One of the objectives of land cover monitoring in
this study is to detect areas which have land cover
changes. If different land cover has different
seasonal vegetation dynamics, GVI data can be used
to detect land cover change.
Seasonal vegetation dynamics in a year by 12
monthly GVI data can be represented by a point in
the 12th dimensional space. Therefore change of
seasonal vegetation dynamics between two different
years can be measured by Euclid distance between
two points in the 12th dimensional space.
Figure 14 shows level-sliced Euclid distance
image between 1983 and 1987. Dark color of the
figure means smaller Euclid distance which explains
that there is no land cover change within this time
period. Red color means larger Euclid distance
which does not directly mean a land cover change in
this area. Because an apparent land cover change
may occur by 'multi-temporal sampling problem’ or
by cloud contamination mentioned in the section
2.1. In the area where land cover is not uniform
in GVI pixel size, that is approximately 20km by
20km, 'multi-temporal sampling problem' causes
wrong vegetation seasonal dynamics and results in
apparent land cover change between two different
years. If some areas are covered by cloud during
the whole month, this causes wrong seasonal
vegetation dynamics and leads to an apparent land
cover change.
8. CONCLUSIONS
Monthly GVI data was found as a strong tool for
global land cover monitoring by their information
of seasonal vegetation dynamics which is
effectively displayed by color composite of
arbitrary three monthly GVI images or principal
component images.
Cluster analysis is a useful technique for
global land cover classification. Asia was
classified into typical seven different land cover
types by cluster analysis of 12 monthly GVI data.
It was found out that simple difference of
monthly GVI data between two different years does
not work for land cover change detection because of
'multi-temporal sampling problem' and cloud
contamination. One solution to cope with
'multi-temporal sampling problem’ is to use GAC
data which are the original data of GVI data. For
the problem of cloud contamination, there are two
approaches, one of which is to detect cloud using
other data and the other is the use of microwave
remote sensing.
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